Spatial omics: current advances, biomedical applications, and future perspectives

Spatial omics is an emerging analytical paradigm that addresses the loss of spatial context inherent in conventional single-cell omics by integrating molecular information with spatial coordinates in intact tissues. This review outlines the conceptual framework and technological evolution of spatial omics and systematically compares major platforms based on next-generation sequencing (NGS) and imaging technologies. Key differences in resolution, analytical scope, and data characteristics are discussed for representative NGS-based approaches, including Visium, high-definition Visium spatial transcriptomic technology (Visium HD), and Stereo-seq, and imaging-based methods, including Xenium, MERSCOPE, and CosMx Spatial Molecular Imager. Recent advances in spatial multi-omics are also highlighted, with emphasis on the integration of transcriptomic, proteomic, and epigenomic data within the same spatial framework. We further describe essential computational strategies, including cell segmentation, spatial domain identification, and inference of cell-cell interactions, and demonstrate how spatial omics data can be translated into biological insights. Applications in oncology and neuroscience are reviewed, illustrating how spatial omics enables characterization of tumor microenvironment heterogeneity, prediction of immunotherapy responses, and investigation of neurodegenerative disease progression. Finally, current challenges, including data complexity, lack of standardization, and high cost, are discussed, along with future perspectives such as integration with artificial intelligence, three-dimensional tissue reconstruction, and real-time in vivo analysis. This review provides a comprehensive foundation for applying spatial omics in precision medicine and next-generation pathology.